QUARANTEAM
Thinking beyond boundaries
Aboubacar
Doumbia
Michael
Bradshaw
Joane
Osei Owusu
Mohammed
Perves
Dhruv
Mehandiratta
Kimberly
Wang
What is our Mission?
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2
Our Mandate
“To provide a strategic plan that optimizes the distribution of the COVID-19 vaccines in a
fair and unbiased manner”
Awareness
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2 3
Prioritization
How do we prioritize
the remaining adults in
Phase 2 & 3 of the
Ontarian rollout?
How do we minimize
wastage of the
vaccine?
Distribution
How will the
population know
when to receive
vaccines?
Current Covid-19 vaccine distribution in Ontario
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11,344 vaccines have
been wasted in 2 weeks
A Covid Vaccine produced
by Pfizer cost $19.50
$221,208
Underlying medical conditions at risk to Covid-19
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Pop Size
Hypertension
Diabetes
Cardiovascular
Disease
Chronic
Obstructive
Lung
Disease
Cancer
Chronic
Kidney
191
30%
31.48%
8%
3%
1.68%
1.68%
54
43.15%
23.36%
24.07%
7.41%
0%
3.70%
137
23.36
-
1.46%
1.46%
7%
-
Awareness Plan
Create a marketing
campaign by utilizing
geo-targeted ads. Also
implement a health
passport
Our Strategy
Provincial Priority Level
Assess
Prioritization Plan
Create a detailed
plan ensuring the
people more at risks
gets vaccinated first
Inject
Scheduling Plan
Establish a website
where users can
schedule an
appointment online
or on the phone
Immunize
Making an impact to flatten the curve
Assess
Vaccine Prioritization Model - At the Individual Level
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Everyone in the province that we have the health data for will
be clustered and assigned a “provincial priority level” (PPL)
based on the cluster they belong to
Clusters are assigned a “priority level” (PPL) ranging from 1 to 10
1 Highest = Highest Priority | 10 = Lowest priority
Priority will be assigned based on the cluster's averages
of following factors (In order):
Age (old = higher priority)
Underlying health conditions
Income (low = higher priority)
We will be using unsupervised machine learning models such
as K-means to cluster into k (we chose 10) clusters based on
the following numerical features:
We will try to predict a PPL for users who have not been
assigned a PPL at the provincial level
These users will have to fill in a questionnaire when they
register to receive their vaccine with information such as:
Age
Obesity (1,0)
Hypertension (1,0)
Diabetes (1,0)
Other health conditions
Postal Code
Family Size
Essential Work
Then they will be classified into the same 10 PPLs
We will be using the following classification algorithms:
Age
Weight
Income
Essential
Worker
MAX V02
Blood Sugar
Level
Cholesterol Level
Blood pressure
Postal Code Density
Family Size
Workplace Size (0
for WFH)
PPL 4
PPL
7
Note: Score is a measure of accuracy of the classifier on PPLs prediction on a
randomly generated mock dataset
Supervised Learning (For people without data)
Unsupervised Learning (For people with data)
Demographics Health Conditions Exposure & Spread
Classifier Score
KNeighborsClassifier 44%
SVC 40%
DecisionTreeClassifier 28%
RandomForestClassifier 32%
AdaBoostClassifier 40%
Classifier Score
GaussianNB 38%
LinearDiscriminantAnalysis 42%
QuadraticDiscriminantAnalysis 30%
NeuralNetwork
N/A
GradientBoostingClassifier 40%
K-means (K=10) Population Cluster’s Characteristics
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Cluster Priority
Level (PPL)
0 (Blue) 6
1 (Orange) 10
2 (Red) 4
3 (Teal) 3
4 (Green) 7
5 (Yellow) 1
6 (Purple) 9
7 (Pink) 8
8 (Brown) 5
9 (Grey) 2
Order of Vaccination by Cluster
5, 9, 3, 2, 8, 0, 4, 7, 6, 1
Order determined by Age, Weight, and Income according to at-risk factors provided by
Public Health Ontario
Expert panel can look at individuals in clusters
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Example
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If we had say the following 4 people register for vaccines at the ABC Shoppers
DrugMart On May 20, 2021
And they had the following PPLs based on their characteristics
Name Age Obesity Hypertension Diabetes Income Family Size PPL Classifier
PPL
Abdul 66 1 0 1 Lower 7 2 N/A
Bob 23 0 0 0 Upper 3 9 N/A
Tyrone 34 0 0 1 Middle 5 6 N/A
Li 56 0 1 1 Middle 6 N/A 3
ABC Shoppers
DrugMart
Appointment Queue
Vaccine Distribution Model - At the PHU Level
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Unsupervised Learning: K-means Clustering
PHUs are clustered using into k (we chose 5) clusters based on the
following Census and Covid features:
Population age: 65+ yrs
Average population age
Population age: 0 - 17 yrs
Population age: 18 - 64 yrs
Population in 2016
Active Covid Cases
Resolved Covid Cases
Covid Deaths
Aboriginal Population
Population in health services
Population in education, law
and social, community and
govt services
Population in trades, transport
and equipment operators and
related occupations
Average total income of
households in 2015
Population living in apartments
in buildings with 5+ storeys
Vaccines can be distributed based on the cluster each PHU
belongs to and the number of people in each PHU that belong to
the Population Clusters
Non-Political & Fair Distribution of Vaccines
AI Ethics: A Pillar of the Prioritization Model
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General Concerns Mitigation
Bias Variables such as income, race, or
religion
Patient Confidentiality on Supervised
Machine Learning Data Collection Forms
Medical Conditions, Private Info, etc.
Is the Data Collection, Transformation, and
Insight Storage Secure?
Will patients be informed of consent
and will they be promised the data
will not be shared other than its
intended use?
Model Transparency
We avoid the use of “Black-Box”
Algorithms and will provide detailed
literature on how the prioritization model
came to a conclusion
Utilize Unbiased Training Data
Prioritize the Investment of Top Notch Data
Security
Develop and Implement Data Consent “Best
Practices” to Reassure the Public that Data
will be Used as Intended
Allow a panel of Experts to view the
characteristics of each cluster, along with the
individuals within the cluster and assign the
priority level in a fair and unbiased manner
Inject
From the Clinics’ Perspective
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Our vision for Scheduling
Each PHU is further broken up into various
inoculation sites
Shoppers Drug Mart, Rexall, etc.
Each PHU will receive a predetermined number of
vaccines per week, based on the prioritization
models
Based on PPL data provided by the Provincial
Government, each PHU will be able to schedule
inoculations based on PPL level
What about Vaccine Wastage?
Each subsidiary inoculation site for each PHU will
have access to the ledger of scheduling for its site
If there are missed appointments, the site can start
contacting patients by PPL level scheduled for the
next day in order to minimize vaccine wastage
Patients that missed their appointment will have to
re-engage with the Covid-19 web app to be
rescheduled
Web App
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Immunize
Marketing Campaign
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Health Passport
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Given to everyone who
had taken the Covid-
19 vaccine
Public places are able to scan
the health passport to know if the
user has received the vaccine
Data will be stored on cloud
with end-to-end encryption
Bob Smith
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3
2
Probability
Impact
1
2
3
People lying about their
medical condition
Significant amount of
people trying to register
on the web app at once
Someone tries to
duplicate the health
passport
Mitigations
Risks
Risks & Mitigations
Cross referencing their
health condition in the
provincial database
Integrate a time ticket
Each health passport will
be given an unique
identifier
Thank you!